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Defining the nature of forensic science and criminalistics is a topic that has been vigorously revisited in international literature. At the core of this discussion is the demarcation of principles that would characterize forensic science as an autonomous science, with its methods. The concept of theoretical synthesis as elaborated by Walker and Avant (2011) was adopted. In this theoretical model, concepts and principles are organized in a coherent and integrated way. In this first paper, the theory synthesis was elaborated from basic concepts and practices related to the work of the forensic scientist. Basically, the procedures for searching and collecting traces and the analytical methods of identification and individualization of traces. The theory synthesis was mainly developed based on: a) the reasoning method adopted in cognitive operations by forensic scientists; b) the way of obtaining information and interpreting phenomena from observation of facts and traces; c) how forensic scientists perform their investigations, analysis or synthesis from evidence and its main practical limitations. In short, the proposed theory synthesis reinforces the idea that forensic science is a specific science, capable not only of applying technologies and using scientific methods but also of producing knowledge inherent to its performance and scope. [ABSTRACT FROM AUTHOR]

Automated fault localization has been extensively studied to improve the effectiveness of software debugging. Existing automated fault localization methods neglect the guidance of the simple and easily available debugging information on fault localization. To bridge manual fault localization with automated fault localization, we propose a fault localization approach combining error guessing and logical reasoning via deep learning. The proposed approach simulates the actual debugging process. Specifically, developers' debugging experience and context dependencies between methods are mapped into two different types of coverage matrices. The constructed matrices are fed to a convolutional neural network (CNN) to predict whether a method is buggy or not. To validate the effectiveness of the proposed approach, we designed and constructed the empirical study on the widely used Defect4J datasets. With respect to the top-n (n = 1 , 3 , 5) metric, our approach outperforms the state-of-the-art DeepFL and other five methods including Ochai, Muse, MULTRIC, TraPT and FLUCSS. Particularly, compared with the above methods, our approach has an improvement of 5–182% for top-1. In terms of MFR and MAR, the proposed approach is slightly lower than the best DeepFL but better than the other five methods. The approach we presented achieving the unification of manual and automatic debugging can aid in the improvement of fault localization accuracy. [ABSTRACT FROM AUTHOR]

The main objective of this literature review is to delve into the situation of coding programs in schools in the Philippines, examining both the obstacles they encounter and potential areas for improvement. By analyzing a range of studies, scholarly articles, and relevant literature, this research aims to comprehend how coding programs impact students' acquisition of skills, logical reasoning abilities, creativity, and cognitive development. Moreover, it evaluates approaches and best practices for integrating coding courses into the curriculum while considering the advantages and challenges associated with their implementation. In addition, the study explored how students' academic performance and future job prospects are influenced by their coding skills and proposed strategies to enhance coding programs that promote the development of these abilities. The insights gathered from this analysis provide recommendations for research and valuable insights into designing coding curricula that cater specifically to elementary schools in the Philippines within the broader educational landscape.

The task of machine reading comprehension (MRC) is to enable machine to read and understand a piece of text and then answer the corresponding question correctly. This task requires machine to not only be able to perform semantic understanding but also possess logical reasoning capabilities. Just like human reading, it involves thinking about the text from two interacting perspectives of semantics and logic. However, previous methods based on reading comprehension either consider only the logical structure of the text or only the semantic structure of the text and cannot simultaneously balance semantic understanding and logical reasoning. This single form of reasoning cannot make the machine fully understand the meaning of the text. Additionally, the issue of sparsity in composition presents a significant challenge for models that rely on graph-based reasoning. To this end, a cross-graph knowledge propagation network (CGKPN) with adaptive connection is presented to address the above issues. The model first performs self-view node embedding on the constructed logical graph and semantic graph to update the representations of the graphs. Specifically, a relevance matrix between nodes is introduced to adaptively adjust node connections in response to the challenge posed by sparse graph. Subsequently, CGKPN conducts cross-graph knowledge propagation on nodes that are identical in both graphs, effectively resolving conflicts arising from identical nodes in different views, and enabling the model to better integrate the logical and semantic relationships of the text through efficient interaction. Experiments on the two MRC datasets ReClor and LogiQA indicate the superior performance of our proposed model CGKPN compared to other existing baselines. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The main objective of this literature review is to delve into the situation of coding programs in schools in the Philippines, examining both the obstacles they encounter and potential areas for improvement. By analyzing a range of studies, scholarly articles, and relevant literature, this research aims to comprehend how coding programs impact students' acquisition of skills, logical reasoning abilities, creativity, and cognitive development. Moreover, it evaluates approaches and best practices for integrating coding courses into the curriculum while considering the advantages and challenges associated with their implementation. In addition, the study explored how students' academic performance and future job prospects are influenced by their coding skills and proposed strategies to enhance coding programs that promote the development of these abilities. The insights gathered from this analysis provide recommendations for research and valuable insights into designing coding curricula that cater specifically to elementary schools in the Philippines within the broader educational landscape. [ABSTRACT FROM AUTHOR]

The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system's situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults. [ABSTRACT FROM AUTHOR]

To diagnose someone's reasoning today as "sophistry" is to say that this reasoning is at once persuasive (at least to a significant degree) and logically invalid. We begin by explaining that, despite some recent scholarly arguments to the contrary, the understanding of 'sophistry' and 'sophistic' underlying such a lay diagnosis is in fact firmly in line with the hallmarks of reasoning proffered by the ancient sophists themselves. Next, we supply a rigorous but readable definition of what constitutes sophistic reasoning (=sophistry). We then discuss "artificial" sophistry: the articulation of sophistic reasoning facilitated by artificial intelligence (AI) and promulgated in our increasingly digital world. Next, we present, economically, a particular kind of artificial sophistry, one embodied by an artificial agent: the lying machine. Afterward, we respond to some anticipated objections. We end with a few speculative thoughts about the limits (or lack thereof) of artificial sophistry, and what may be a rather dark future. [ABSTRACT FROM AUTHOR]

The experience of meaning has been found to be mapped onto spatial proximity whereby coherent—in contrast to incoherent—elements in a set are mentally represented as closer together in physical space. In a series of four experiments, we show that spatial representation of coherence is malleable and can employ other meaningful concrete dimensions of space that are made salient. When given task instructions cueing verticality, participants represented coherence in the upper vertical location when making judgements about the logical validity of realistic (Experiments 1 and 4) and unrealistic syllogistic scenarios (Experiment 3). When the task instruction made the spatial proximity between the stimuli materials and the participant salient (subjective proximity), participants represented coherence as spatially close to themselves (Experiment 2). We also found that being accurate in judging the validity of syllogisms was associated with representing coherence in the upper visual field or close to oneself. Overall, our findings show that identifying semantic links between an abstract concept and a given spatial dimension involves using that dimension to create spatial metaphoric mappings of the concept.

Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural‐network‐based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof‐of‐concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non‐symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Logical table‐to‐text generation is a task within the realm of natural language generation (NLG) that aims to generate coherent and logically faithful sentences based on tables. Unlike conventional NLG tasks, this task demands not only surface‐level fluency but also a high degree of logic‐level fidelity in the generated outputs. Current table‐to‐text systems grapple with various quality issues, such as repetitive generation, insufficient reasoning and limited complexity. Therefore, we introduce LogicMoE, a dedicated Mixture‐of‐Experts (MoE) model tailored for logical table‐to‐text generation. The primary objective of LogicMoE is to enrich the diversity of generated sentences from both semantic and logical perspectives. In particular, each expert within the model serves as a specialized generator responsible for generating sentences of a specific logical type. Additionally, we propose and employ novel evaluation metrics to comprehensively assess the diversity of generated outputs. Our experimental results showcase LogicMoE's superiority with absolute improvements of 0.8 and 2.2 in BLEU‐3 over the strong baselines on LogicNLG and Logic2Text datasets, respectively, driving the state‐of‐the‐art performance to a new level. Furthermore, we highlight its inherent advantages in terms of diversity and controllability, signifying its potential to spearhead advancements in logical table‐to‐text generation applications. [ABSTRACT FROM AUTHOR]

Mathematics learning (ML) is a fundamental aspect of education that lays the groundwork for various academic disciplines and practical applications. Understanding the factors that propel ML is crucial for optimizing educational outcomes. This quantitative empirical study investigates the impact of logical reasoning (LR), critical thinking (CT), information technology (IT), and distance learning (DL) on ML. The study employs structural equation modeling (SEM) using SmartPLS 4 for data analysis and hypothesis testing. The findings reveal that LR, CT, IT, and DL positively influence ML. The results highlight the importance of fostering LR, CT, and the integration of IT in mathematics education. This study contributes to the existing body of knowledge by providing insights into the factors that promote effective ML. These findings have implications for educators, policymakers, and curriculum developers, aiding in the design of instructional strategies and the integration of technology to enhance ML outcomes. [ABSTRACT FROM AUTHOR]

Meta-moral cognitive skills consist of identifying reasons behind ethical decisions, potential criticisms for such reasons, and constructing counterarguments for these criticisms. We assessed the relationship among these three elements of ethical judgment justification using ethical dilemmas. A mixed-methods research design was used to investigate university students from India and Japan. Critical thinking skills, knowledge of professional ethics, discipline, perspective-taking, common sense, and culture influenced the respondents’ meta-moral cognitive skills. There was a correlation between the number/strength of reasons and criticisms and between criticisms and counterarguments. The respondents had difficulty connecting the reasons for their decisions and their counterarguments. The results imply that incorporating personal and professional cases as part of the ethics curriculum can improve meta-moral cognitive skills. [ABSTRACT FROM AUTHOR]

Mathematics is an important skill that all students should have the opportunity to learn. Using effective teaching methods and creating a positive learning environment helps students succeed in mathematics. In this study, we worked with a sample of 60 third-year students of Basic General Education from the Buena Esperanza Educational Unit in Ecuador. They were distributed into two groups: experimental and control. An educational intervention was carried out where the experimental group of 30 students was trained; the intervention was focused on developing students' logical reasoning. The results showed that children who trained their logical reasoning made more progress in mathematics than a control group that did not receive this training. It was observed that the students' learning was effective, their motivation and participation were high during the intervention. The teachers found the proposed strategy viable. According to the opinions of the teachers and experts consulted, the strategy has been described as easily applicable, fun and effective. [ABSTRACT FROM AUTHOR]

Automated Vehicles (AVs) are designed and programmed to follow traffic rules. However, there is no separate and comprehensive regulatory framework dedicated to AVs. The current Queensland traffic rules were designed for humans. These rules often contain open texture expressions, exceptions, and potential conflicts (conflict arises when exceptions cannot be handled in rules), which makes it hard for AVs to follow. This paper presents an automatic compliance checking framework to assess AVs behaviour against current traffic rules by addressing these issues. Specifically, it proposes a framework to determine which traffic rules and open texture expressions need some additional interpretation. Essentially this enables AVs to have a suitable and executable formalization of the traffic rules. Defeasible Deontic Logic (DDL) is used to formalize traffic rules and reasoning with AV information (behaviour and environment). The representation of rules in DDL helps effectively in handling and resolving exceptions, potential conflicts, and open textures in rules. 40 experiments were conducted on eight realistic traffic scenarios to evaluate the framework. The evaluation was undertaken both quantitatively and qualitatively. The evaluation result shows that the proposed framework is a promising system for checking Automated Vehicle interpretation and compliance with current traffic rules. [ABSTRACT FROM AUTHOR]

Background. Pain is known to negatively impact attention, but its influence on more complex cognitive abilities, such as logical reasoning, remains inconsistent. This may be due to compensatory mechanisms (e.g., investing additional resources), which might not be detectable at the behavioral level but can be observed through psychophysiological measures. In this study, we investigated whether experimentally induced pain affects logical reasoning and underlying attentional mechanisms, using both behavioral and electroencephalographic (EEG) measures. Methods. A total of 98 female participants were divided into a pain-free control group (N = 47) and a pain group (N = 51). Both groups completed the Advanced Progressive Matrices (APM) task, with EEG recordings capturing task-related power (TRP) changes in the upper alpha frequency band (10–12 Hz). We used a mixed design where all participants completed half of the APM task in a pain-free state (control condition); the second half was completed under pain induction by the pain group but not the pain-free group (experimental condition). Results. Logical reasoning performance, as measured by APM scores and response times, declined during the experimental condition, compared to the control condition for both groups, indicating that the second part of the APM was more difficult than the first part. However, no significant differences were found between the pain and pain-free groups, suggesting that pain did not impair cognitive performance at the behavioral level. In contrast, EEG measures revealed significant differences in upper alpha band power, particularly at fronto-central sites. In the pain group, the decrease in TRP during the experimental condition was significantly smaller compared to both the control condition and the pain-free group. Conclusions. Pain did not impair task performance at the behavioral level but reduced attentional resources, as reflected by changes in upper alpha band activity. This underscores the importance of incorporating more sensitive psychophysiological measures alongside behavioral measures to better understand the impact of pain on cognitive processes.

People are more likely to think statements are valid when they agree with them than when they do not. We conducted four studies analyzing the interference of self-reported ideologies with performance in a syllogistic reasoning task. Study 1 established the task paradigm and demonstrated that participants' political ideology affects syllogistic reasoning for syllogisms with political content but not politically irrelevant syllogisms. The preregistered Study 2 replicated the effect and showed that incentivizing accuracy did not alleviate these differences. Study 3 revealed that syllogistic reasoning is affected by ideology in the presence and absence of such bonus payments for correctly judging the conclusions' logical validity. In Study 4, we observed similar effects regarding a different ideological orientation: environmentalism. Again, monetary bonuses did not attenuate these effects. Taken together, the results of four studies highlight the harm of ideology regarding people's logical reasoning. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to investigate the impact of a Problem-Based Learning (PBL) course on cognitive skills (i.e., Critical Thinking, Problem-Solving, Logical Reasoning, Creativity, and Decision-Making) in the context of solving geometric construction problems. Methods: The research utilized a quasi-experimental design involving a control group and an experimental group to assess the effects of the PBL intervention. Cognitive skills were measured using a custom-designed questionnaire. Additionally, Structural Equation Modeling (SEM) was employed in a subsequent phase to scrutinize the causal interrelationships among these cognitive skills. Results: In the initial phase, the findings revealed that the PBL intervention had a statistically significant positive impact on problem-solving and creativity skills. However, the effects on critical thinking, logical reasoning, and decision-making skills did not reach statistical significance. In the subsequent phase employing SEM, the analysis demonstrated significant positive relationships, particularly between critical thinking and problem-solving, critical thinking and logical reasoning, logical reasoning and problem-solving, and logical reasoning and creativity. Notably, creativity also exhibited a significant positive effect on problem-solving. Discussion: This study underscores the nuanced impact of PBL on different cognitive skills, with clear enhancements observed in problem-solving and creativity. However, the study suggests that the effects may not be uniform across all cognitive skills. These findings offer valuable insights for educators and curriculum designers, emphasizing the need for tailored approaches when integrating PBL to foster cognitive skill development. [ABSTRACT FROM AUTHOR]

This research article is based on the master's thesis titled "OfiCalc and GeoGebra as didactic tools for the understanding, use, and application of angles in 10th-grade students at the I.E. Gabriel García Márquez in San Carlos de Guaroa, Meta Department, Colombia." The study emphasizes the significance of Realistic Mathematics Education (rme) in the teaching and learning of Mathematics, along with its influence on the transformation of the educational curriculum and compliance with the General Education Law (Law 115 of 1994) in Colombia. The main purpose of the article is to evaluate the implementation of this approach in the Colombian educational context, considering existing legal regulations and curriculum guidelines. To achieve this, a qualitative literature review is conducted, facilitating an analysis of the shared principles and foundations between rme and Law 115 of 1994, exploring their relationship with the teaching and learning of mathematics. Additionally, a detailed analysis is provided regarding the importance of promoting rme as a pedagogical approach aimed at enhancing the quality and relevance of mathematical education in Colombia. As a result, the need to establish connections between mathematical concepts and the real world is emphasized, along with the presentation of authentic mathematical challenges to stimulate critical thinking, logical reasoning, and informed decision-making. [ABSTRACT FROM AUTHOR]

The article focuses on document-level relation extraction extending the identification of entity relations from single sentences to entire documents which poses new challenges in relation representation and reasoning. Topics included "Separate Relation Representation," "Logical Reasoning," and addressing challenges in indirect relation representation and massive interaction support for document-level relation extraction.

Logical reasoning of context is vital for reading comprehension, which requires to explore the logical relationship through sentence structure. However, previous methods of logical symbols and graph-based models do not make full explore the relationships among entities. In this paper, we present a verb-driven dual-graph network (VDGN) that utilizes core verbs of sentences to model the inter-sentence relationship by the ability of verbs to express linguistic context and the shortest dependency path to model the relationship between entities of intra-sentence. We construct a context graph and a query graph respectively through the above method. In order to predict the answer correctly, our framework fuses information from the context graph and the query graph applying a bi-directional attention mechanism on graph data. We evaluate our approach on two public logical reasoning machine reading comprehension(MRC) datasets: ReClor and LogiQA. Experiments on representative benchmark datasets demonstrate the effectiveness of our approach. • Verbs and dependency parser can solve logical reasoning problems in text. • Verbs can express linguistic context and the logical relationship between entities. • Graph-based and bi-directional attention can enhance the model's text comprehension. [ABSTRACT FROM AUTHOR]

Objective: In advanced educational systems, attention to efficient and fundamental educational methods is the core educational goal and the evaluation of the effectiveness of these procedures is continuously conducted. Therefore, the present study aimed to design a critical thinking instruction program and determine the effectiveness of critical thinking instruction program in cognitive and behavioral outcomes in second year high school students. Methods: The present study was conducted with a semi-experimental design (an unequal control group and pre-test-post-test). The study population included 11th grade female students in Tabriz city in the academic year 2021-2022. Using multi-stage random cluster method, two classes (30 subjects) were selected and one class was assigned as an experimental group and the other one as control group. For the experimental group, critical thinking strategies were taught during 10 sessions, and the control group did not receive any intervention. During pre-test and post-test, dependent variables were measured using Beck and J. Sapp's multidimensional questioning scale, activity evaluation form for California, Kember et al.'s logical thinking questionnaire, as well as Cassidy and Long's problem solving questionnaire. Results: Data were analyzed by multivariate and univariate analysis of covariance. Results showed that critical thinking instruction program was effective in the improvement of the ability of questioning, logical reasoning, and problem solving in high school students (F=34.12, p<0.05). However, this instruction program was not effective in the ability to evaluate. Conclusions: Hence, it can be concluded that cognitive skills, especially questioning, logical reasoning, and the ability to solve problems through systematic training program, could be changed and manipulated, and the systematic application of critical thinking could facilitate the attainment of these outcomes. [ABSTRACT FROM AUTHOR]

With the rapid development of machine learning, challenging question and answer datasets have also emerged, and the machine reading comprehension technology has emerged. Traditional machine reading comprehension methods mostly focus on the understanding word level semantics, with the weak ability to extract logical relationships from text, resulting in the lower ability of logical reasoning. In order to strengthen the ability of machine reading comprehension method to extract the logical relationship of text and the ability of logical reasoning, a neural symbol model based on logical reasoning was proposed, and the logical expressions captured by the neural symbol model were converted into text input and trained in a mixed reasoning reading comprehension model based on symbolic logic. The mixed reasoning reading comprehension model based on symbolic logic is different from the traditional machine reading comprehension model. It uses symbolic definition and logical capture to extract logical symbols and generate logical expressions. The research results show that the accuracy and F-measure values of the neural symbol model based on the logical reasoning are 70.08% and 70.05%, respectively, when the training set sample size is 4000. The accuracy of the mixed reasoning reading comprehension model based on symbolic logic in the logical reasoning data set of the standard postgraduate entrance examination is 88.31%, which is higher than the 58.74% of the language perception map network model. The accuracy rate in the four-choice and one-choice question-and-answer data set is 40.92%, which is 1.58% higher than that of the language awareness graph network model. In summary, the neural symbol model and hybrid inference reading comprehension model proposed in the study have superior performance, which can capture the logical relationship of text in data sets well, improve the model feature abstraction and reasoning ability, effectively shorten the training time and improve the model efficiency.

The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system’s situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults.

This paper logically explores the overall form of ideological and political education system construction in colleges and universities, constructs the logical rationale of system cognition and development, and explores the operation process of system dynamic balance. The ideological and political education system is constructed through the comprehensive planning of multiple data chains, and the demand for teaching tasks and communication delays is solved through optimized planning. Maximizing the utilization of teaching resources is achieved by optimizing the deployment of teaching resources. Finally, the Civics teaching mode of multi-data chain integrated planning was evaluated and tested through experiments. The results show that the three dimensions of the pre-test of the experimental group are 4.29, 4.21 and 4.41, respectively, which are improved to 5.60, 5.83 and 4.99, with a significant improvement effect. This paper effectively explores the development of Civics teaching, realizes all-round education, and achieves the fundamental purpose of cultivating morality.

IntroductionThis study aims to investigate the impact of a Problem-Based Learning (PBL) course on cognitive skills (i.e., Critical Thinking, Problem-Solving, Logical Reasoning, Creativity, and Decision-Making) in the context of solving geometric construction problems.MethodsThe research utilized a quasi-experimental design involving a control group and an experimental group to assess the effects of the PBL intervention. Cognitive skills were measured using a custom-designed questionnaire. Additionally, Structural Equation Modeling (SEM) was employed in a subsequent phase to scrutinize the causal interrelationships among these cognitive skills.ResultsIn the initial phase, the findings revealed that the PBL intervention had a statistically significant positive impact on problem-solving and creativity skills. However, the effects on critical thinking, logical reasoning, and decision-making skills did not reach statistical significance. In the subsequent phase employing SEM, the analysis demonstrated significant positive relationships, particularly between critical thinking and problem-solving, critical thinking and logical reasoning, logical reasoning and problem-solving, and logical reasoning and creativity. Notably, creativity also exhibited a significant positive effect on problem-solving.DiscussionThis study underscores the nuanced impact of PBL on different cognitive skills, with clear enhancements observed in problem-solving and creativity. However, the study suggests that the effects may not be uniform across all cognitive skills. These findings offer valuable insights for educators and curriculum designers, emphasizing the need for tailored approaches when integrating PBL to foster cognitive skill development.

Several authors have suggested theoretical approaches that support the practices of forensic scientists, including adherence to the evidential paradigm and the centrality of the concept of trace, both included in the Sydney Declaration. The objective of this paper was to continue the construction of an integrated theoretical model, which incorporates the epistemological, methodological and practical dimensions of the forensic scientist’s work. Therefore, these new discussions found in the literature will be incorporated, as well as some established traditional concepts. A synthesis theory was elaborated from basic concepts and practices related to the following procedures performed by forensic scientists: a) use of laws derived from other sciences to assert causes associated with state changes observed in a trace; b) use of inferences and experimentation for the reconstruction of the criminal event and detection of trace arrangement patterns in criminal scenarios. The formulation of a coherent, hierarchical and systematic framework provides subsidies for facing some challenges in forensic science, such as: evaluating the role of cognitive bias in certain phases of the forensic scientist’s work; the determination of a coherent curriculum that aggregates the essential competences for forensic analysis and the improvement of the predictive potential of forensic science in intelligence studies. [ABSTRACT FROM AUTHOR]

In recent years, with the advancement of natural language processing techniques and the release of models like ChatGPT, how language models understand questions has become a hot topic. In handling complex logical reasoning with pre-trained models, its performance still has room for improvement. Inspired by DAGN, we propose an improved DaGATN (Discourse-apperceptive Graph Attention Networks) model. By constructing a discourse information graph to learn logical clues in the text, we decompose the context, question, and answer into elementary discourse units (EDUs) and connect them with discourse relations to construct a relation graph. The text features are learned through a discourse graph attention network and applied to downstream multiple-choice tasks. Our method was evaluated on the ReClor dataset and achieved an accuracy of 74.3%, surpassing the best-known performance methods utilizing deberta-xlarge-level pre-trained models, and also performed better than ChatGPT (Zero-Shot). [ABSTRACT FROM AUTHOR]

Background: Children's numerical and arithmetic skills differ greatly already at an early age. Although research focusing on accounting for these large individual differences clearly demonstrates that mathematical performance draws upon several cognitive abilities, our knowledge concerning key abilities underlying mathematical skill development is still limited. Aims: First, to identify key cognitive abilities contributing to children's development of early arithmetic skills. Second, to examine the extent to which early arithmetic performance and early arithmetic development rely on different or similar constellations of domain‐specific number abilities and domain‐general cognitive abilities. Sample: In all, 134 Swedish children (Mage = 6 years and 4 months, SD = 3 months, 74 boys) participated in this study. Method: Verbal and non‐verbal logical reasoning, non‐symbolic number comparison, counting knowledge, spatial processing, verbal working memory and arithmetic were assessed. Twelve months later, arithmetic skills were reassessed. A latent change score model was computed to determine whether any of the abilities accounted for variations in arithmetic development. Results: Arithmetic performance was supported by counting knowledge, verbal and non‐verbal logical reasoning and spatial processing. Arithmetic skill development was only supported by spatial processing. Conclusions: Results show that young children's early arithmetic performance and arithmetic development are supported by different cognitive processes. The findings regarding performance supported Fuchs et al.'s model (Dev Psychol, 46, 2010b, 1731) but the developmental findings did not. The developmental findings align partially to Geary et al.'s (J Educ Psychol, 109, 2017, 680) hypothesis stating that young children's early arithmetic development is more dependent on general cognitive abilities than number abilities. [ABSTRACT FROM AUTHOR]

In deep learning, large language models are typically trained on data from a corpus as representative of current knowledge. However, natural language is not an ideal form for the reliable communication of concepts. Instead, formal logical statements are preferable since they are subject to verifiability, reliability, and applicability. Another reason for this preference is that natural language is not designed for an efficient and reliable flow of information and knowledge, but is instead designed as an evolutionary adaptation as formed from a prior set of natural constraints. As a formally structured language, logical statements are also more interpretable. They may be informally constructed in the form of a natural language statement, but a formalized logical statement is expected to follow a stricter set of rules, such as with the use of symbols for representing the logic-based operators that connect multiple simple statements and form verifiable propositions.

To diagnose someone’s reasoning today as “sophistry” is to say that this reasoning is at once persuasive (at least to a significant degree) and logically invalid. We begin by explaining that, despite some recent scholarly arguments to the contrary, the understanding of ‘sophistry’ and ‘sophistic’ underlying such a lay diagnosis is in fact firmly in line with the hallmarks of reasoning proffered by the ancient sophists themselves. Next, we supply a rigorous but readable definition of what constitutes sophistic reasoning (=sophistry). We then discuss “artificial” sophistry: the articulation of sophistic reasoning facilitated by artificial intelligence (AI) and promulgated in our increasingly digital world. Next, we present, economically, a particular kind of artificial sophistry, one embodied by an artificial agent: the lying machine. Afterward, we respond to some anticipated objections. We end with a few speculative thoughts about the limits (or lack thereof) of artificial sophistry, and what may be a rather dark future.

Abstract Irrespective of much research examining the effects of ego depletion on self-control related measures in social psychology, inadequate attention has been paid regarding whether ego depletion influences on logical reasoning performance. To address this gap, this experimental study randomly assigned Chinese college students (n = 112) to either an ego-depletion or control condition. After manipulating self-control strength through typing words without an e-task, they were instructed to work on logical reasoning questions as well as surveys assessing their cognitive, motivational, and metacognitive processes. The results supported our hypotheses regarding logical reasoning and monitoring accuracy, as depleted students were significantly outperformed by non-depleted students. Furthermore, mental effort was a significant mediator between the ego depletion manipulation and logical reasoning performance. Our findings contribute to the ongoing debate about the existence of the ego depletion effect. Theoretical and practical implications are discussed and more future research is needed. [ABSTRACT FROM AUTHOR]

Copyright of Revista Internacional de Estudios en Educación is the property of Revista Internacional de Estudios en Educacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An event logic graph is a kind of knowledge mapping technology for knowledge inference and simulation analysis, which takes events as the core and portrays the hierarchical system and logical evolution pattern between events. In order to apply it to further improve the accuracy of related studies, such as pedestrian flow evacuation, simulation model optimization and risk prediction. In this paper, we use social network resources, media resources and journal database resources to build our corpus and adopt the explicit event relationship extraction method based on syntactic dependency and the implicit event relationship extraction method based on BERT+Bi-LSTM+Attention+Softmax for the characteristics of explicit event relationship and implicit event relationship, respectively. This paper constructs a pedestrian flow evacuation matter mapping for three typical scenarios and discusses its application path. It is found that once a sound knowledge base of logical reasoning and event logic graph is established, both research on optimization of pedestrian flow evacuation simulation models and research on identification and assessment of pedestrian flow evacuation safety risks will receive excellent support. [ABSTRACT FROM AUTHOR]

Definition: In deep learning, large language models are typically trained on data from a corpus as representative of current knowledge. However, natural language is not an ideal form for the reliable communication of concepts. Instead, formal logical statements are preferable since they are subject to verifiability, reliability, and applicability. Another reason for this preference is that natural language is not designed for an efficient and reliable flow of information and knowledge, but is instead designed as an evolutionary adaptation as formed from a prior set of natural constraints. As a formally structured language, logical statements are also more interpretable. They may be informally constructed in the form of a natural language statement, but a formalized logical statement is expected to follow a stricter set of rules, such as with the use of symbols for representing the logic-based operators that connect multiple simple statements and form verifiable propositions. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The principle of Al-Zati La-Yo’allal (Self-evident essence does not need proof) is one of the important philosophical doctrines which falls into three philosophical rules. This paper intends to review Imam Khomeini’s analysis of this principle with a glance at the lexical connotation of the word Zati. In view of Imam Khomeini “an essence needless of proof”, the core concept of this principle, is an essence coming from logical reasoning rather than accidentality. Indeed, Imam Khomeini is of the opinion that “an essence needless of proof” is a truth inseparable from inherent essence whether it is imbedded in inherent essence (Isagoge essence) or detached but associated with it. Imam Khomeini continues with explaining about why self-evident essence does not need proof, i.e. he argues “an essence needless of proof” through logical reasoning. Therefore, paying attention to his precise opinion on this principle can help answering many philosophical, theological, etc. questions. Apparently, Imam Khomeini has underscored this point that since it is imperative to carry self-evident essences and attributes of essence on essence itself, and because any attribute affixed to its subject is needless of proof, the self-evident essences and attributes of essence do not need proof at all, hence any question on its proof shall be meaningless.

This theoretical paper examines whether acquiring better literacy skills may help people to be less vulnerable to various types of information disorders such as fake news or conspiracy theories. We start this venture by looking, first, at the relationships between vulnerability to information disorders and literacy (or, more generally, formal education), and second, at the literacy-induced transfer effects on cognitive skills. Then we will comment on some interesting findings made within the framework of an experimental project in which we examined subliterate adults and adolescents. Grounded on these results, we conjecture on the relationships between literacy, logical reasoning, critical thinking and endorsement of conspiracy theories.